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Predicting Signal Peptides with Support Vector Machines

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Pattern Recognition with Support Vector Machines (SVM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

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Abstract

We examine using a Support Vector Machine to predict secretory signal peptides. We predict signal peptides for both prokaryotic and eukaryotic signal organisms. Signalling peptides versus non-signaling peptides as well as cleavage sites were predicted from a sequence of amino acids. Two types of kernels (each corresponding to different metrics) were used: hamming distance, a distance based upon the percent accepted mutation (PAM) score trained on the same signal peptide data.

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Author information

Authors and Affiliations

  1. Center for Biological and Computational Learning, Massachusetts Institute of Technology, 45 Carleton St., Cambridge, MA, 02139

    Neelanjan Mukherjee & Sayan Mukherjee

  2. Dept. of Biology, University of California San Diego, USA

    Neelanjan Mukherjee

  3. Center for Genome Research, Whitehead Institute Massachusetts Institute of Technology, USA

    Sayan Mukherjee

Authors
  1. Neelanjan Mukherjee
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  2. Sayan Mukherjee
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

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© 2002 Springer-Verlag Berlin Heidelberg

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Mukherjee, N., Mukherjee, S. (2002). Predicting Signal Peptides with Support Vector Machines. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_1

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  • DOI: https://doi.org/10.1007/3-540-45665-1_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

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